Managed digital workforce for software development
A pipeline of AI agents carries a task from idea to a verified code change. A code graph guards the architecture; an enterprise control layer governs access and spend. The platform deploys inside your own perimeter and runs on local or sovereign models.
Companies already pay for AI in development, but they buy subscriptions, not a process
An assistant for every developer
Spend grows with headcount, and results depend on whoever is at the keyboard. The team-level effect cannot be measured.
Quality and architecture go unchecked
Agents write code faster than people can read it. Tests stay green while module boundaries and security rules quietly break.
Code cannot leave the perimeter
Banks, industry and state-owned companies do not send code to external services. Foreign models are blocked or banned; local and sovereign models are required.
Customers do not pay for "digital headcount". They pay for a managed process: lead time, quality, control and predictable spend.
Three products that work as one system
Adopted one after another: first the pipeline in one team, then the code graph, then the control layer for the whole company.
Rabotnik
A pipeline of AI agents inside a development team. A task is set in chat, and the pipeline takes it the rest of the way: ticket, specification, tests, code, review, acceptance.
in production since May 2026GoCPG
A code property graph: module dependencies, change impact, vulnerabilities, architecture violations. In review, the pipeline checks every change against the graph and catches what tests miss: a crossed module boundary or a broken architecture rule.
live demo at codegraph.tech →CodeGraph
An enterprise layer above teams: shared requirements and roles, requirement-to-test traceability, leak prevention, cost accounting.
next stageKey decisions stay with people: a human approves the specification and accepts the result, and the author of a change never accepts it. All three products share one platform layer: the graph, project memory with rules learned from mistakes, model routing, control and audit.
Agents do the work, people make the decisions
The model follows the role: a strong one writes specifications and reviews, a cheap or local one implements. If a model is unavailable or out of quota, the role falls through to the next in line: another provider or a local model. A fully local setup with no external models is proven in practice.
The platform develops itself every day
in daily production use: a team server and workstations on Linux, macOS and Windows, one codebase and one release.
tasks opened and closed in October: about 100 closed tasks a week at flat subscription spend.
models on 3 engines. The model is chosen per role, so the product does not depend on a single vendor.
paid pilot running right now: the pipeline works inside a live customer team.
Industrial holding
Pilot and a 100-seat licence for 2027 under negotiation.
Pharmacy chain
Paid pilot running, with a decision on an annual contract ahead.
5 more companies in the pipeline: a software vendor, a marketplace, an information security company, a telecom operator and a data company. Customer names are disclosed only with their consent.
A process on top of the tools, and a code graph that assistants do not have
| Alternative | What it gives | How we differ |
|---|---|---|
| Developer assistants: Copilot, Cursor, GigaCode | help one person write code | we run the whole team's process; an assistant can be one of the executors inside the pipeline |
| Agent platforms: Yandex AI Studio, Cloud.ru, MWS | a general-purpose agent builder | a ready-made development process, a code graph and a methodology rather than a builder |
| Enterprise agent dashboards: Microsoft Agent 365, Workday | agent inventory and permissions | unavailable inside closed perimeters; our control is built into the development process itself |
| Personal proactive agents: Claude Duet (Anthropic, preview since 21 Oct 2026), ChatGPT Dots, Meta Muse | an agent for one person: writes first, works while you are away, asks before acting | Rabotnik has worked on the same idea since May 2026, but for a team: each member's personal agent runs tasks on a shared board and answers in the team chat |
What keeps its value however the model market evolves: the code graph, project memory and rules learned from mistakes, model routing, on-premises installation, connectors to Git, CI and trackers.
A per-team licence that grows inside the company
One month, one team. Before-and-after metrics agreed upfront.
Per team of up to 10 seats.
One-time installation inside the customer perimeter.
The CodeGraph enterprise layer is priced separately. Inference is paid by the customer: their own model subscriptions or local models.
Three founders
Alexander Kotin
Ex-Postgres Pro: Postgres Pro Enterprise core development and deployments at the largest banks.
Mikhail Savin
Head of AI at Postgres Pro; product management for Yandex Managed DB.
Konstantin Solovyev
CEO: enterprise sales, negotiations, partnerships.
Run a pilot with your team
One month, one team, real tasks from its backlog, inside your perimeter. We reply within one business day.